article · Business Strategy and the Environment
Many businesses have set carbon neutrality targets to address climate risks and meet regulatory standards, traditionally relying on non-digital and process-driven methods. A qualitative study exploring how enterprises use artificial intelligence to achieve these goals identifies four core dimensions of AI adoption. First, companies deploy AI for both direct and indirect emissions control. Second, they navigate strategic trade-offs involving funding, data, system requirements, and social priorities. Third, organisations must overcome human and organisational barriers to adoption. Fourth, they recognise significant gains in business model efficiency and measurable progress towards net-zero targets. The resulting convergence-divergence model outlines the positive drivers, inhibitors, synergies, and trade-offs that determine how effectively firms can deploy AI to reach carbon neutrality.
As regulatory pressures mount, organisations need effective tools to reduce carbon emissions. While businesses frequently rely on operational process changes, digital technologies offer alternative mechanisms. Understanding the operational trade-offs, organisational hurdles, and performance benefits of artificial intelligence provides corporate leaders with a clearer view of how digital tools can support verifiable progress towards carbon neutrality targets.
This research informs corporate sustainability managers, enterprise software developers, and business strategists seeking to design or implement artificial intelligence solutions for carbon emissions management. Because the work delivers a qualitative, conceptual convergence-divergence framework based on experiences from existing firms, it is an early-stage strategic guide rather than a deployable technology, offering insights into operational and organisational considerations for future enterprise software adoption.
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Abstract Many firms have established formal carbon neutrality (CN) targets in response to the increasing climate risk and related regulatory requirements. Subsequently, they have implemented various measures and adopted multiple approaches to attain these goals. Academic research has given due attention to firms' efforts in this direction. However, past studies have primarily focused on non‐digital and process‐oriented approaches to achieving CN, with the potential of digital technologies such as artificial intelligence (AI) remaining less explored. Our study aims to address this gap by qualitatively examining the use of AI for pursuing CN, drawing insights from firms with prior experience in the area. We analyzed the collected qualitative data to identify four key dimensions that capture different nuances of applying AI for achieving CN: (a) implementing AI for direct and indirect control of emissions, (b) accepting the strategic trade‐offs related to funding, data and systems concerns, and social priorities, (c) overcoming organizational and human‐related impediments, and (d) acknowledging the significant impact of AI in terms of gains in business model efficiency and measurable CN target attainment, which ultimately contribute to CN. Based on our findings, we propose a convergence–divergence model encompassing the positive aspects, inhibiting factors, synergies, and offsets necessary for firms to leverage AI to achieve net‐zero emissions effectively. Overall, our study contributes to the discourse on the utilization of AI for CN in a comprehensive manner.
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DOI: 10.1002/bse.3689
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